bdm::migamma Class Reference

Inverse-Gamma random walk. More...

#include <libEF.h>

List of all members.

Public Member Functions

void set_parameters (int len, double k0)
 Set value of k.
void condition (const vec &val)
 Update ep so that it represents this mpdf conditioned on rvc = cond.
Constructors
 migamma ()
 migamma (const migamma &m)
Matematical operations
virtual vec samplecond (const vec &cond)
 Returns a sample from the density conditioned on cond, $x \sim epdf(rv|cond)$.
virtual mat samplecond_m (const vec &cond, int N)
 Returns.
virtual double evallogcond (const vec &dt, const vec &cond)
 Shortcut for conditioning and evaluation of the internal epdf. In some cases, this operation can be implemented efficiently.
virtual vec evallogcond_m (const mat &Dt, const vec &cond)
 Matrix version of evallogcond.
Access to attributes
RV _rv ()
RV _rvc ()
int dimension ()
int dimensionc ()
epdf_epdf ()
epdf_e ()
Connection to other objects
void set_rvc (const RV &rvc0)
void set_rv (const RV &rv0)
bool isnamed ()

Protected Attributes

eigamma epdf
 Internal epdf that arise by conditioning on rvc.
double k
 Constant $k$.
vec & _alpha
 cache of epdf.alpha
vec & _beta
 cache of epdf.beta
int dimc
 dimension of the condition
RV rvc
 random variable in condition
epdfep
 pointer to internal epdf


Detailed Description

Inverse-Gamma random walk.

Mean value, $ \mu $, of this density is given by rvc . Standard deviation of the random walk is proportional to one $ k $-th the mean. This is achieved by setting $ \alpha=\mu/k^2+2 $ and $ \beta=\mu(\alpha-1)$.

The standard deviation of the walk is then: $ \mu/\sqrt(k)$.


Member Function Documentation

virtual vec bdm::mpdf::samplecond ( const vec &  cond  )  [inline, virtual, inherited]

Returns a sample from the density conditioned on cond, $x \sim epdf(rv|cond)$.

Parameters:
cond is numeric value of rv

Reimplemented in bdm::mprod.

References bdm::mpdf::condition(), bdm::mpdf::ep, and bdm::epdf::sample().

Referenced by bdm::MPF< BM_T >::bayes(), bdm::PF::bayes(), and bdm::ArxDS::step().

virtual mat bdm::mpdf::samplecond_m ( const vec &  cond,
int  N 
) [inline, virtual, inherited]

Returns.

Parameters:
N samples from the density conditioned on cond, $x \sim epdf(rv|cond)$.
cond is numeric value of rv

References bdm::mpdf::condition(), bdm::epdf::dimension(), bdm::mpdf::ep, and bdm::epdf::sample().


The documentation for this class was generated from the following file:

Generated on Thu Apr 23 21:06:46 2009 for mixpp by  doxygen 1.5.8